IoT Applications for Healthcare Systems

✍ Scribed by Rahul K. Kher, Chirag Paunwala, Falgun Thakkar, Heena Kher, Mita Paunwala, Prasan Kumar Sahoo, Larif Ladid

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 161
• Series: EAI/Springer Innovations in Communication and Computing
• Category: Library

✦ Synopsis

This book discusses communications technologies used in the field of healthcare, including IoT, soft computing, machine learning, big data, augmented reality, and wearable sensors. The book presents various applications that are helpful for research scholars and scientists who are working toward identifying and pinpointing the potential of this technology. The book also helps researchers and practitioners to understand and analyze the e-healthcare architecture through IoT and the state-of-the-art in IoT countermeasures with real-time challenges. Topics of interest include healthcare systems based on advanced development boards, mobile health parameters recording and monitoring systems, remote health / patient monitoring, hospital operations management, abnormality / disease detection by IoT devices, and efficient drug management. The book is relevant to a range of researchers, academics, and practitioners working on the intersection of IoT and healthcare.

✦ Table of Contents

Preface
Contents
Internet of Medical Things: Applications and Research Issues in Healthcare Monitoring
1 Introduction
1.1 Internet of Medical Things (IoMT)
1.2 Contributions
2 IoMT Healthcare Frameworks
2.1 IoM Things Layer
2.2 Communication Layer
2.2.1 Short-Range Communication Protocols
2.2.2 Long-Range Communication Protocols
2.2.3 Emergency Communication Through CRN
2.3 Computation Layer
2.3.1 IoMT Big Data Analysis
2.3.2 Artificial Intelligence
3 IoMT in Healthcare Monitoring
3.1 IoMT Applications
3.1.1 Body-Centric Applications
3.1.2 Object-Centric Applications
3.2 Advantages of IoMT
3.2.1 Remote Health Monitoring
3.2.2 Automatic Health Parameters Sensing
3.2.3 Distributed Access
3.2.4 Improved Self-Care Diagnostic
3.2.5 Reduce Unnecessary Medical Tests
3.2.6 Enhance the Quality of Healthcare Management
3.2.7 Affordable
4 Research Issues and Future Directions
4.1 Energy Consumption Minimization
4.2 Precision and Accuracy Acquirement
4.3 Resilient Communication Establishment
4.4 Cloud or Fog Computing Adaptation
4.5 Big IoMT Data Analysis and Management
4.6 Intelligent Decision Making Through AI
4.7 Security and Privacy
4.8 Maintenance of Quality of Service (QoS)
4.9 Scalability
4.10 Comparable Cost Analysis
4.11 Environmental Sustainability
4.12 Interoperability
4.13 Generic IoMT Framework
4.14 Hardware Constraint of Wearable IoMT Devices
4.15 User-Friendly App Development
4.16 Device's Unique Identification
4.17 Addition of Blockchain Technology
4.18 Introduction of Dew Computing
5 Conclusions
Copyright Statement
Copyright
References
Role of Internet of Things and Artificial Intelligence in COVID-19 Pandemic Monitoring
1 Introduction
2 Applications of AI in COVID-19 Monitoring
2.1 Early Detection Phase of Pandemic
2.2 Diagnosing Phase of Pandemic
2.3 Monitoring Phase of Pandemic
3 IoT in COVID-19 Monitoring
3.1 Role of IoT
3.2 IoT Data Acquisitions
4 AI in COVID-19 Monitoring
4.1 Data Preprocessing
4.2 Data Analysis
5 Challenges of IoT and AI in New Normal
5.1 IoT Big Data in Smart City
5.2 AI Development for Monitoring Pandemic
6 Conclusion
References
Design of LinkIt ONE-Based IoT System with Middleware Architecture for Healthcare Monitoring
1 Introduction
2 Literature Survey
3 Sensor Node with Master-Slave Architecture and Design Criteria
4 Hardware Development
4.1 Requirements to Select Microcontroller Board
4.2 Main Features
4.3 Specifications
4.4 Embedded C Language for Arduino IDE of LinkIt ONE SDK
4.5 Sensors for Slave/Secondary Unit of Sensor Node Design
4.6 Technologies Used for Master/Primary Unit
5 Results
6 Middleware Development
7 Survey on Middleware and Communication Protocols of IoT-Based System
References
FPGA Implementation of Multivariate Support Vector Regression for Non-invasive Blood Glucose Estimation Using IoMT Framework
1 Introduction
2 Prior Work for Blood Glucose Measurement
2.1 Data Set of Blood Glucose Measurement for Regression Model
3 Support Vector Machine for Blood Glucose Prediction
3.1 Support Vector Machine-Based Regression
3.2 Gamma and Cost Parameters
3.2.1 Cost Parameters
3.2.2 Gamma Parameters
3.3 FPGA Implementation of SVR Decision Function for Glucose Estimation
4 Results and Discussion
5 Conclusion and Future Work
References
IoT – A New Paradigm for Healthcare Monitoring
1 Evolution of Healthcare Systems and Technology
2 IoT in Healthcare
2.1 Advantages of IoT in Healthcare
2.2 IoT-Based Healthcare Applications
2.2.1 Emergency Alert Systems
2.2.2 Heart Monitors and Reporting
2.2.3 Ingestible Sensors
2.2.4 Medicine Dispensers
2.2.5 Trackable Inhalers
2.2.6 Remote Monitoring
2.2.7 Location Services
3 The IoT Healthcare Models
3.1 Healthcare Using IoT and Cloud Computing
3.1.1 Wearable Sensor and Central Nodes
3.1.2 Short-Range Communications
3.1.3 Long-Range Communications
3.1.4 Secure Cloud Storage with Machine Learning
3.1.5 Big Data Management
3.1.6 Security and Privacy of the Cloud
3.2 Internet of Things (IoT) in Hospitals
4 IoT-Based Healthcare System: Challenges and Issues
4.1 Data Security and Privacy
4.2 Integration: Multiple Devices and Protocols
4.3 Data Overload and Accuracy
4.4 Cost
5 Conclusion
References
e-Healthcare Challenges: Scenario in Rural Regions of South Asia
1 Introduction
2 E-Healthcare Scenario in South Asia
2.1 E-Health Scenario in Rural Bangladesh
2.2 E-Health Scenario in Rural Sri Lanka
2.2.1 Acceptance to Change
2.2.2 Availability
2.2.3 Staff Involvement
2.2.4 Policies and Standards
2.3 E-Health Scenario in Rural India
2.3.1 Barriers to e-Healthcare in Rural India
2.4 E-Health Scenario in Rural Malaysia
2.5 E-Health Scenario in Rural Thailand
3 Challenges in e-Healthcare: A South Asian Scenario
3.1 ICT Infrastructure
3.2 Basic ICT Knowledge and Skills
3.3 Training of Human and Skilled Workforce
3.4 Reluctance to Novel Technology
3.5 Perceptions
3.6 Internet
3.7 Electric Power Supply
3.8 Financial and Sustainability Issues
3.9 Funding
3.10 Data Protection and Confidentiality
4 Possible Solutions
5 Conclusion
References
Computation Offloading for Smart Healthcare Applications
1 Introduction
2 Related Work
2.1 Fog Computing
2.2 Prominent Fog Application
2.3 Edge Computing
2.4 Cloudlet
3 Computation Offloading in Edge Environment
3.1 Types of Computation Offloading
3.2 Offloading Policy
4 Mathematical Challenges
5 Numerical Results
5.1 Coordinate Descent for Edge Computing
5.2 Deep Reinforcement Learning for Edge Computing
6 Conclusion
References
Change and Periodic Events: Relevance to the Pandemic
1 Introduction
2 Why Is Spectral Statistics Important?
3 Connecting Theories Together
3.1 Simple Harmonic Motion (SHM)
3.2 String Theory
3.3 Differential Equations
4 Spectrum Analysis via Fourier Transform
4.1 Analysis of COVID-19
5 Summary and Conclusions
References
Index